Title:Residual Conv-Deconv Grid Network for Semantic Segmentation

Abstract: This paper presents GridNet, a new Convolutional Neural Network (CNN)
architecture for semantic image segmentation (full scene labelling). Classical
neural networks are implemented as one stream from the input to the output with
subsampling operators applied in the stream in order to reduce the feature maps
size and to increase the receptive field for the final prediction. However, for
semantic image segmentation, where the task consists in providing a semantic
class to each pixel of an image, feature maps reduction is harmful because it
leads to a resolution loss in the output prediction. To tackle this problem,
our GridNet follows a grid pattern allowing multiple interconnected streams to
work at different resolutions. We show that our network generalizes many well
known networks such as conv-deconv, residual or U-Net networks. GridNet is
trained from scratch and achieves competitive results on the Cityscapes
dataset.